Potential of typical highland and mountain forests in the Czech Republic for climate-smart forestry: ecosystem-scale drought responses
Bibliographic record
Abstract
Climate-smart forestry (CSF) consists of an extensive framework of actions directed to mitigating and adapting to global climate change impacts on the resilience and productivity of forest ecosystems. This study investigates the impact of the pan-European 2018 drought on carbon exchange dynamics in typical highland and mountain forests in the Czech Republic, including two coniferous stands (Norway spruce at Bílý Kříž and Rajec) and one deciduous stand (European beech at Štítná). Our results show that the annual net ecosystem CO2 uptake at Rajec decreased by 50% during the drought year in comparison to a reference year with normal climatic conditions. The Bílý Kříž stand is less affected by drought, as the local microclimate ensures sufficient water supply. The European beech forest at Štítná is most resilient against drought and its negative impacts — there we detect no differences in carbon exchange dynamics between the drought year and the reference year. We consider the matching of tree species to site conditions as crucial in the context of CSF, specifically regarding the stand response to water limitation and water supply and demand. Successively replacing spruce with beech trees in areas with high water demand but limited water supply, like Rajec, will support the goals of CSF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".